{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3c39dfd4-40cb-4a5f-b41c-4ecc839545ed",
   "metadata": {},
   "source": [
    "箱线图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4efd0936-2f07-4318-b855-5a74d84eee1d",
   "metadata": {},
   "source": [
    "箱形图（Box Plot），也称为盒须图，是一种用于展示数据分布特征的统计图\n",
    "表。箱形图能够直观地展示数据的关键指标，包括下四分位数（Q1）、上四\r\n",
    "位数（Q3）、中位数和异常值点。它通过箱体和须线的方式，将数据的中位\r\n",
    "置、离散程度、对称性和异常值情况清晰地描述出来。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e3c5ef-3cdb-4022-8ec9-59dea43d7f2e",
   "metadata": {},
   "source": [
    "sns.catplot(\r\n",
    "    data=None, *,\r\n",
    "    x=None, y=None, hue=None, row=None, col=None,\r\n",
    "    col_wrap=None, estimator=<function mean>, ci=95, \r\n",
    "n_boot=1000,\r\n",
    "    units=None, order=None, hue_order=None, row_order=None, \r\n",
    "col_order=None,\r\n",
    "    kind='box', height=5, aspect=1, orient=None, color=None, \r\n",
    "palette=None,\r\n",
    "    legend=True, legend_out=True, sharex=True, sharey=True, \r\n",
    "margin_titles=False,\r\n",
    "    facet_kws=None, **kwargs\r\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "917bf9db-4770-4a67-9a7b-0be8559a3ff0",
   "metadata": {},
   "source": [
    "主要参数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735ba9da-0775-43a5-a1da-b8a7b378c667",
   "metadata": {},
   "source": [
    "data : DataFrame，数据集。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d00d2a7-28e2-4873-aac5-d6905d1bbd90",
   "metadata": {},
   "source": [
    "x , y : 分别对应于横轴和纵轴的数据变量名。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "116474c7-de11-4e12-b15b-58100a823a64",
   "metadata": {},
   "source": [
    "hue : 分类变量名，用于区分不同类别的数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db1363d-3e03-45c0-8475-fccc45af8f1b",
   "metadata": {},
   "source": [
    "row , col : 分类变量名，用于创建子图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2fff481-b2e3-46e6-a156-285c34119c21",
   "metadata": {},
   "source": [
    "col_wrap : int，当有多个列子图时，设置每行的最大列数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3141afe-e5f6-4b87-8461-a218ca3dd597",
   "metadata": {},
   "source": [
    "estimator : 函数，用于计算中心趋势。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6eca2e4-bd5e-4aa2-ae5f-c3447625b20b",
   "metadata": {},
   "source": [
    "ci : 置信区间大小，默认95。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bbd4ca2-dc93-4082-b8d8-09ee64aeb366",
   "metadata": {},
   "source": [
    "n_boot : 引导法的重采样次数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cadc574b-152a-4b8e-9048-063f351b9b4e",
   "metadata": {},
   "source": [
    "units : 确定计算置信区间的单位变量。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c08001-4eea-456d-bb20-b6989e6b5803",
   "metadata": {},
   "source": [
    "order : list，指定绘制的类别顺序。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77be30b-44cd-470a-8ec5-6b5af238d5f0",
   "metadata": {},
   "source": [
    "hue_order : list，指定 hue 变量的类别顺序"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95c1b6e-2759-47fd-8208-9b84a36fc027",
   "metadata": {},
   "source": [
    "row_order , col_order : list，指定 row 和 col 变量的类别顺序"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "487b70b1-134b-4324-a60b-d0002bea3732",
   "metadata": {},
   "source": [
    "kind : 图表类型，设置为 \"box\" 。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f18b6b1-74af-42e4-b44f-9ac03d23788d",
   "metadata": {},
   "source": [
    "height : 单个子图的高度（英寸）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "276ce8c4-9b7e-4560-b5b9-80c7d1d81d58",
   "metadata": {},
   "source": [
    "aspect : 子图宽高比，默认值为1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "973d71db-9194-467f-bf6b-8e8866c65d94",
   "metadata": {},
   "source": [
    "orient : 方向，可选值为 \"v\" 或 \"h\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f59191aa-b622-4f1a-bb75-acd4d0298f2a",
   "metadata": {},
   "source": [
    "color : 单一颜色。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b850bb5-2429-45aa-a6d9-a59b27392410",
   "metadata": {},
   "source": [
    "palette : 调色板，指定不同类别的颜色"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c6e6dd-c627-41ad-b292-e5a02bc2eb34",
   "metadata": {},
   "source": [
    "legend : 是否显示图例，布尔值。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80a8bcb2-47c4-4887-b179-2e6e4671fb46",
   "metadata": {},
   "source": [
    "legend_out : 图例是否显示在图表外部，布尔值。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dba77d1-05f6-4226-87b1-25f5f4c8a701",
   "metadata": {},
   "source": [
    "sharex , sharey : 子图是否共享 x 或 y 轴，布尔值"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a22f157-68fa-4ff5-adc1-ccfcf352c7a3",
   "metadata": {},
   "source": [
    "margin_titles : 是否为子图标注标题，布尔值。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73856b80-01e4-4952-a857-ecc7f8eb6a2d",
   "metadata": {},
   "source": [
    "facet_kws : dict，传递给 FacetGrid 的其他参数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7528ea72-f742-405d-8a77-9e775ff3dd30",
   "metadata": {},
   "source": [
    "**kwargs : 传递给 sns.boxplot 的其他参数。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "042daa8c-8b5a-4de9-b61c-46c8ac39cc21",
   "metadata": {},
   "source": [
    "【1】"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f05e7e24-72fd-4af7-b86b-3d742729936f",
   "metadata": {},
   "source": [
    "普通箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0051fc20-a46e-48ee-bdd5-3d54db50e26b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 511.111x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotx\n",
    "import pandas as pd\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "\n",
    "titanic = pd.read_csv('./seaborn数据集/titanic.csv')\n",
    "plt.style.use(matplotx.styles.pitaya_smoothie['light'])\n",
    "sns.catplot(  # 使用catplot（kind=‘box’）方法\n",
    "    data=titanic,\n",
    "    x=\"年龄\", \n",
    " #   y=\"年龄\", # 箱子沿着水平方向\n",
    "    kind=\"box\",  # kind指定箱型图\n",
    "    width=0.1,  # 设置箱子宽度\n",
    "    fliersize=10,  # 设置异常值点大小\n",
    "    flierprops={   # 异常值点属性个性化\n",
    "        'marker':'o',  # 点形状\n",
    "        'markerfacecolor':'#b1283a',  # 填充颜色\n",
    "        'color':'cyan',  # 点外部颜色\n",
    "        'linewidth':5,  # 线宽\n",
    "        'alpha':1  # 透明度\n",
    "    },\n",
    "   # showfliers=False,  # 关闭异常值\n",
    "    capprops={   # 上下边缘线属性个性化\n",
    "        'linestyle':'dotted',  # 线型\n",
    "        'color':'#a8a6a7',  # 线颜色\n",
    "        'linewidth':1, # 线宽\n",
    "        'alpha':1  # 透明度\n",
    "    },\n",
    "    showcaps=False,  # 关闭上下边缘线\n",
    "    whiskerprops={   # 须触线属性个性化\n",
    "        'linestyle':'--',  # 线型\n",
    "        'color':'#a8a6a7', # 线颜色\n",
    "        'linewidth':1,   # 线宽\n",
    "        'alpha':0.8  # 透明度\n",
    "    },\n",
    "    boxprops={\n",
    "        'facecolor':'#a8a6a7',  # 箱体内部填充的颜色\n",
    "        'edgecolor':'#a8a6a7',  # 箱体边缘线的颜色\n",
    "        'linewidth':1,   # 箱体边缘线的线宽\n",
    "        'linestyle':'-',  # 箱体边缘线的线型\n",
    "        'alpha':0.6   # 箱体内部填充的透明度\n",
    "    },\n",
    "    medianprops={   # 中位数属性个性化\n",
    "        'linestyle':'--',  # 线型\n",
    "        'color':'#b1283a', # 颜色\n",
    "        'linewidth':1,  #线宽\n",
    "        'alpha':0.8  # 透明度\n",
    "    },\n",
    "    showmeans=True, # 显示平均数   \n",
    "#    meanprops={  # 平均数个性化\n",
    " #       'marker':'o',  # 点形状\n",
    "  #      'markerfacecolor':'#b1283a',  # 点填充颜色\n",
    "   #     'markeredgecolor':2,  # 点外部线宽\n",
    "    #    'markersize':12  # 点大小\n",
    "    #},  \n",
    "    meanline=True, # 显示平均数线\n",
    "    meanprops={  # 平均数个性化\n",
    "        'linestyle':'--',  # 线型\n",
    "        'color':'#b1283a',  # 线颜色 \n",
    "        'linewidth':1  # 线宽\n",
    "    },\n",
    "    linewidth=1, # 设置所以的线宽\n",
    "    # 添加置信区间，凹口表示围绕中位数的置信区间（CI），位置默认通过高斯的渐进逼近算法计算\n",
    "    notch=True,\n",
    "    # 当设置bootstrap时，通过bootstrap随机抽样计算凹口位置，介于1000到10000\n",
    "    bootstrap=10000,\n",
    "    color='#a8a6a7'\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b708b80-8d7e-4ef6-859a-23aa84982cb1",
   "metadata": {},
   "source": [
    "箱形图中的凹口（notch）表示的是围绕中位数的置信区间（Confidence \n",
    "Interval, CI），它能提供关于中位数稳定性的信息。引入 notch 和 bootstrap\r\n",
    "参数有助于更详细地描述和比较数据的中位数。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b2247c4-36ce-4907-9d94-e1b01dba0bb1",
   "metadata": {},
   "source": [
    "notch=True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b7fbef-05f9-44c4-abf7-db489e1667d8",
   "metadata": {},
   "source": [
    "notch=True 参数用于在箱形图中绘制凹口。凹口的宽度表示的是中位数的置信\n",
    "区间。其主要优点是"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c369a62f-f17e-416e-a0fe-74303a8dbde9",
   "metadata": {},
   "source": [
    "1. 中位数的稳定性: 凹口可以显示围绕中位数的置信区间，从而帮助判断中位\n",
    "数是否显著不同。如果两个箱形图的凹口不重叠，则可以推断它们的中位\r\n",
    "有显著差异。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fc8b38d-e790-4de7-ae3c-29eb20215ca7",
   "metadata": {},
   "source": [
    "2. 视觉比较: 凹口使得不同组间中位数的比较更加直观和容易理解。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be8bd83-3630-4fc4-8399-58aae34d08f7",
   "metadata": {},
   "source": [
    "凹口的宽度通常通过高斯的渐近逼近算法（asymptotic approximation）计\n",
    "算。这种算法可以有效估计中位数的标准误差，从而确定置信区间。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb7cc3b-aeeb-4cb1-95cd-5f097b1da380",
   "metadata": {},
   "source": [
    "bootstrap=10000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e4b10ff-ad39-44b8-8e67-28b1aaeaa29e",
   "metadata": {},
   "source": [
    "bootstrap=10000 参数用于通过引导法（Bootstrap）来计算凹口的位置。引\n",
    "导法是一种非参数统计方法，通过反复随机抽样来估计统计量的分布。设置这\r\n",
    "参数的目的是"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7d39ae5-dc73-4e81-9286-6cd4b887f562",
   "metadata": {},
   "source": [
    "1. 提高准确性: 尽管高斯渐近逼近算法在大多数情况下是有效的，但引导法可\n",
    "以提供更稳健的置信区间估计，特别是在样本量较小或分布偏斜的情况下。\r\n",
    "2. 灵活性: 引导法不依赖于数据的分布假设，因此可以更灵活地应用于各数\r\n",
    "据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3dafa09-22d3-46df-8920-ac2e474f1f62",
   "metadata": {},
   "source": [
    "设置 bootstrap=10000 表示进行10000次随机抽样来估计中位数的置信区间。\n",
    "这种方法可以提高凹口位置估计的准确性和稳健性"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d65d4d-717a-4dfc-822e-4c214bf7122f",
   "metadata": {},
   "source": [
    "notch=True: 用于绘制凹口，表示中位数的置信区间，有助于比较不同组间\n",
    "的中位数。\r\n",
    "bootstrap=10000: 通过引导法计算凹口位置，提高置信区间估计的准性\r\n",
    "和稳健性。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5340596-6281-40ac-9d20-edcd1e2509f5",
   "metadata": {},
   "source": [
    "【2】"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b38d2d7-84ae-4eb1-99a4-90141906f369",
   "metadata": {},
   "source": [
    "分组箱形图-垂直方向"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19da0ecc-5771-46ed-8039-b263c61739dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 591.75x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(\n",
    "    data=titanic,\n",
    "    x=\"登船港口\",  # 设置垂直方向，此时虽然设置的是x轴横轴坐标，但是影响的是垂直方向\n",
    "    y=\"年龄\",  \n",
    "    hue=\"性别\", # 分组，箱子颜色随性别变化\n",
    "    width=0.5,  # 箱子原始间隔\n",
    "    linewidth=0.6,  \n",
    "    order=[\"Q\",\"S\",\"C\"],  # Q、S、C为登船港口    设置箱子顺序为qsc\n",
    "    kind=\"box\",  \n",
    "    palette=\"Accent\", \n",
    " #   palette=['#006a8e','#b1283a']\n",
    "    saturation=0.5  # 设置颜色饱和度\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3bd568-938f-435e-8d99-fe45c5841586",
   "metadata": {},
   "source": [
    "【3】"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df9c8595-14e0-47b9-b34e-a194fdb93fb2",
   "metadata": {},
   "source": [
    "分组箱形图-按行按列分面多子图 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c816c39b-13d8-4cd8-99d5-bb938bf0524e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1591.75x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(\n",
    "    data=titanic,\n",
    "    x=\"登船港口\",  # 设置垂直方向，此时虽然设置的是x轴横轴坐标，但是影响的是垂直方向\n",
    "    y=\"年龄\",  \n",
    "    hue=\"性别\", # 分组，箱子颜色随性别变化\n",
    "    col='船票等级', # 列按照船票等级分面\n",
    "    row='获救情况', # 行按照获救情况分面\n",
    "    width=0.7, \n",
    "    linewidth=0.6,  \n",
    "    kind=\"box\",   \n",
    "    palette=['#006a8e','#b1283a']\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
